class_docstr: MobileNet-V3
  
  A this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the
  head convolution without a final batch-norm layer before the classifier.
  
  Paper: https://arxiv.org/abs/1905.02244
code: def as_sequential(self):
      layers = [self.conv_stem, self.bn1, self.act1]
      layers.extend(self.blocks)
      layers.extend(
          [
              self.global_pool,
              self.conv_head,
              self.act2,
              nn.Flatten(),
              nn.Dropout(self.drop_rate),
              self.classifier,
          ]
      )
      return nn.Sequential(*layers)
  
imports: import torch.nn
  import torch.nn.functional
  from .activations import get_act_fn, get_act_layer, HardSwish
  from .config import layer_config_kwargs
  from .conv2d_layers import select_conv2d
  from .helpers import load_pretrained
  from .efficientnet_builder import *
label_desc: Use image node_id 2 for calling the MobileNetV3 obj's underlying member method code with example usage: obj.as_sequential() and returns: nn